import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
plt.style.use('seaborn')
data=pd.read_csv('processed_data.csv')
data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
'DaysSinceFirstStay']]
#Choosing optimal clusters using elbow plot:
from sklearn.cluster import KMeans
wcss = []
for k in range(1,10):
kmeans = KMeans(n_clusters=k)
kmeans.fit(data1)
wcss.append(kmeans.inertia_)
wcss
[595518.9947542043, 450711.5823636649, 386045.12816301436, 337874.5909241742, 305381.0448057603, 277377.47623965674, 261209.24443012773, 245497.25538219645, 231138.56892098451]
# Visualization of k values:
plt.plot(range(1,10), wcss, color='red',marker='x')
plt.title('Graph of k values and WCSS')
plt.xlabel('k values')
plt.ylabel('wcss values')
plt.show()
from yellowbrick.cluster import KElbowVisualizer
kmeans=KMeans(random_state=42)
visual=KElbowVisualizer(kmeans, k=(1,10))
visual.fit(data1)
visual.poof()
<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>
#GAP statistics:
import scipy
class TWHGapStat(object):
def generate_random_data(self, X):
reference = scipy.random.random_sample(size=(X.shape[0], X.shape[1]))
return reference
def _fit_cluster(self,X, n_cluster, n_iter=10):
iterations = range(1, n_iter + 1)
ref_inertias = pd.Series(index=iterations)
for iteration in iterations:
clusterer = KMeans(n_clusters=n_cluster, n_init=3, n_jobs=-1)
clusterer.fit(X)
ref_inertias[iteration] = clusterer.inertia_
mean_nertia = ref_inertias.mean()
return mean_nertia
def fit(self,X,max_k):
k_range = range(1,max_k + 1)
gap_stat = pd.Series(index=k_range)
ref_data = self.generate_random_data(X)
for k in k_range:
base_clusterer = KMeans(n_clusters=k,n_init = 3, n_jobs = -1)
base_clusterer.fit(X)
ref_intertia = self._fit_cluster(ref_data,k)
cur_gap = scipy.log(ref_intertia - base_clusterer.inertia_)
gap_stat[k] = cur_gap
return gap_stat
if __name__ == "__main__":
gap_stat = TWHGapStat()
gs = gap_stat.fit(data1,10)
print (gs)
1 13.213672 2 12.916616 3 12.777263 4 12.606560 5 12.497102 6 12.391419 7 12.326836 8 12.261577 9 12.193390 10 12.169244 dtype: float64
plt.plot(range(len(gs)), gs)
plt.show()
from sklearn.metrics import silhouette_score
sc=[]
kmeans=KMeans(n_clusters=2).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=3).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=4).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=5).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=6).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=7).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=8).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=9).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
kmeans=KMeans(n_clusters=10).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)
len(sc)
9
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(range(2, 11), sc, color='blue')
ax.set_title('Silhouette Coefficients for K_Means')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('Silhouette Coefficient')
Text(0, 0.5, 'Silhouette Coefficient')
# K value = 4
from yellowbrick.cluster import SilhouetteVisualizer
model = KMeans(4, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(data1)
visualizer.show()
<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 63670 Samples in 4 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>
# Initializes the model with selected K, fit and predict the labels for the dataset
kmeans = KMeans(n_clusters=4, random_state=42).fit(data1)
labels_km_1 = kmeans.predict(data1)
# Creates dataframe with the labels
kmeans_df = pd.DataFrame(labels_km_1, columns=['cluster_id'])
# Create plot
fig = plt.figure(figsize=(5, 5))
kmeans_df.value_counts().sort_index().plot.bar()
plt.xlabel('Cluster', fontsize=13)
plt.title('K-Means clusters size distribution', fontsize=14)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','K 4'], rotation=0)
plt.show()
# Dimensionality reduction
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture
# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)
explained_variance = pca.explained_variance_
# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()
pca.explained_variance_ratio_
array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
3.44155412e-05])
#Scree plot
import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))
num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100
ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)
for i in range(9):
ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))
ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')
ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)
(-1.624643895711861, 37.56515055083923)
pca = PCA().fit(data1)
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)
fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')
plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '99% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()
[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055 0.99891343 0.99996558 1. ]
pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)
wcss = []
for k in range(1,10):
kmeans = KMeans(n_clusters=k)
kmeans.fit(x_7cols)
wcss.append(kmeans.inertia_)
wcss
[594871.9215496404, 450064.81788699894, 385397.46616398153, 337230.4544508495, 304819.9400492358, 276731.34842181613, 260563.21786454768, 244855.48117241397, 230492.67405957662]
x_7cols.shape
(63670, 7)
# Visualization of k values:
fig,ax = plt.subplots()
ax.plot(range(1,10), wcss, color='red',marker='x')
plt.axvline(x=4,color='blue',linestyle='--')
plt.title('Graph of k values and WCSS')
plt.xlabel('k values')
plt.ylabel('wcss values')
ax.annotate('elbow=4',xy=(4.2,450000),xytext=(4.5,450000),
horizontalalignment='left',
verticalalignment='top',size=16)
plt.show()
# Initializes the model with selected K, fit and predict the labels for the dataset
kmeans = KMeans(n_clusters=4, random_state=42).fit(x_7cols)
labels_km = kmeans.predict(x_7cols)
# Creates dataframe with the labels
kmeans_df = pd.DataFrame(labels_km, columns=['cluster_id'])
# Create plot
fig = plt.figure(figsize=(5, 5))
kmeans_df.value_counts().sort_index().plot.bar()
plt.xlabel('Cluster', fontsize=13)
plt.title('K-Means clusters size distribution', fontsize=14)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','K 4'], rotation=0)
plt.show()
unscaled_data=pd.read_csv('Unscaled_data.csv')
df = unscaled_data.copy(deep=True)
df['label'] = labels_km
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 63670 entries, 0 to 63669 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Nationality 63670 non-null object 1 Age 63670 non-null float64 2 DaysSinceCreation 63670 non-null float64 3 AverageLeadTime 63670 non-null int64 4 LodgingRevenue 63670 non-null float64 5 OtherRevenue 63670 non-null float64 6 BookingsCanceled 63670 non-null float64 7 BookingsNoShowed 63670 non-null float64 8 BookingsCheckedIn 63670 non-null float64 9 PersonsNights 63670 non-null float64 10 RoomNights 63670 non-null float64 11 DaysSinceLastStay 63670 non-null float64 12 DaysSinceFirstStay 63670 non-null float64 13 DistributionChannel 63670 non-null object 14 MarketSegment 63670 non-null object 15 SRHighFloor 63670 non-null int64 16 SRLowFloor 63670 non-null int64 17 SRAccessibleRoom 63670 non-null int64 18 SRMediumFloor 63670 non-null int64 19 SRBathtub 63670 non-null int64 20 SRShower 63670 non-null int64 21 SRCrib 63670 non-null int64 22 SRKingSizeBed 63670 non-null int64 23 SRTwinBed 63670 non-null int64 24 SRNearElevator 63670 non-null int64 25 SRAwayFromElevator 63670 non-null int64 26 SRNoAlcoholInMiniBar 63670 non-null int64 27 SRQuietRoom 63670 non-null int64 28 month 63670 non-null int64 29 year 63670 non-null int64 30 Day 63670 non-null object 31 label 63670 non-null int32 dtypes: float64(11), int32(1), int64(16), object(4) memory usage: 15.3+ MB
unscaled_data['PersonsNights'].unique()
array([ 8., 10., 4., 6., 3., 14., 1., 2., 12., 9., 5., 11., 13.,
7.])
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],palette="tab10")
plt.legend()
plt.show()
plt_1=plt.figure(figsize=(15,55))
sns.relplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],
col=df['PersonsNights'],palette="tab10",col_wrap=3)
plt.show()
<Figure size 1080x3960 with 0 Axes>
from sklearn import metrics
from sklearn.metrics import davies_bouldin_score
#SILHOUETTE SCORE
metrics.silhouette_score(data1, labels_km, metric='euclidean')
0.22984897613849073
#Calinski-Harabasz Index
#BEFORE PCA
metrics.calinski_harabasz_score(data1, labels_km_1)
16182.805709398132
#AFTER PCA
metrics.calinski_harabasz_score(x_7cols, labels_km)
16213.43252396753
#Davies-Bouldin Index
#BEFORE PCA
davies_bouldin_score(data1, labels_km_1)
1.4089534977687062
#AFTER PCA
davies_bouldin_score(x_7cols, labels_km)
1.4101170804318928
DECISION TREE CLASSIFIER
df_k = data1.copy(deep=True)
df_k['label'] = labels_km
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
X = df_k.drop(['label'],axis=1)
y = df_k[['label']]
# dividing X, y into train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
tuned_paramaters = [{'criterion': ['entropy', 'gini'],
'max_depth': range(2, 10),
'max_features': ["sqrt", "log2"],
'min_samples_split': range(2,10),
'min_samples_leaf': range(1,10),
'max_leaf_nodes': range(1, 10)}]
decision_tree_classification = DecisionTreeClassifier(random_state = 10)
tree_grid = GridSearchCV(estimator = decision_tree_classification,
param_grid = tuned_paramaters,
cv = 5)
tree_grid_model = tree_grid.fit(X_train, y_train)
print('Best parameters for decision tree classifier: ', tree_grid_model.best_params_, '\n')
Best parameters for decision tree classifier: {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'max_leaf_nodes': 8, 'min_samples_leaf': 1, 'min_samples_split': 2}
# training a DescisionTreeClassifier
dtree_model = DecisionTreeClassifier(criterion= 'gini', max_depth= 4, max_features= 'sqrt',
max_leaf_nodes= 8, min_samples_leaf=1, min_samples_split= 2).fit(X_train, y_train)
dtree_predictions_test = dtree_model.predict(X_test)
dtree_predictions_train = dtree_model.predict(X_train)
# creating a confusion matrix
cm = confusion_matrix(y_test, dtree_predictions_test)
score={}
#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,dtree_predictions_train))
precision recall f1-score support
0 0.95 0.46 0.62 6011
1 0.89 0.83 0.86 8892
2 0.90 0.95 0.93 16220
3 0.82 0.96 0.89 16629
accuracy 0.87 47752
macro avg 0.89 0.80 0.82 47752
weighted avg 0.88 0.87 0.86 47752
x=round(accuracy_score(y_train,dtree_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,dtree_predictions_train))
Train accuracy 0.8709373429385157
#Test accuracy:
print(classification_report(y_test,dtree_predictions_test))
precision recall f1-score support
0 0.95 0.45 0.61 2042
1 0.87 0.82 0.85 2912
2 0.89 0.95 0.92 5502
3 0.82 0.96 0.88 5462
accuracy 0.86 15918
macro avg 0.88 0.79 0.81 15918
weighted avg 0.87 0.86 0.85 15918
y=round(accuracy_score(y_train,dtree_predictions_train),2)
print('Test accuracy',accuracy_score(y_test,dtree_predictions_test))
Test accuracy 0.8638019851740169
score.update({'DecisionTree':[x,y]})
NAIVE BAYES CLASSIFIER
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB().fit(X_train, y_train)
gnb_predictions_train = gnb.predict(X_train)
gnb_predictions_test = gnb.predict(X_test)
#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,gnb_predictions_train))
precision recall f1-score support
0 0.91 0.93 0.92 6011
1 0.88 0.95 0.91 8892
2 0.97 0.96 0.96 16220
3 0.97 0.93 0.95 16629
accuracy 0.94 47752
macro avg 0.93 0.94 0.94 47752
weighted avg 0.94 0.94 0.94 47752
x1=round(accuracy_score(y_train,gnb_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,gnb_predictions_train))
Train accuracy 0.9433740995141565
#Test accuracy:
print(classification_report(y_test,gnb_predictions_test))
precision recall f1-score support
0 0.92 0.93 0.93 2042
1 0.87 0.96 0.91 2912
2 0.97 0.96 0.96 5502
3 0.98 0.93 0.95 5462
accuracy 0.94 15918
macro avg 0.93 0.94 0.94 15918
weighted avg 0.95 0.94 0.95 15918
y1=round(accuracy_score(y_test,gnb_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,gnb_predictions_test))
Test accuracy 0.9448423168739791
score.update({'NaiveBayes':[x1,y1]})
KNN CLASSIFIER
from sklearn.neighbors import KNeighborsClassifier
tuned_paramaters = {'n_neighbors': np.arange(1, 25, 2),
'metric': ['hamming','euclidean','manhattan','Chebyshev']}
knn_classification = KNeighborsClassifier()
knn_grid = GridSearchCV(estimator = knn_classification,
param_grid = tuned_paramaters,
cv = 5,
scoring = 'accuracy')
knn_grid.fit(X_train, y_train)
print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\n')
Best parameters for KNN Classifier: {'metric': 'manhattan', 'n_neighbors': 7}
knn = KNeighborsClassifier(n_neighbors = 7,metric='manhattan').fit(X_train, y_train)
knn_predictions_train = knn.predict(X_train)
knn_predictions_test = knn.predict(X_test)
#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,knn_predictions_train))
precision recall f1-score support
0 0.98 0.97 0.97 6011
1 0.98 0.99 0.98 8892
2 0.99 0.98 0.99 16220
3 0.99 0.99 0.99 16629
accuracy 0.99 47752
macro avg 0.98 0.98 0.98 47752
weighted avg 0.99 0.99 0.99 47752
x2=round(accuracy_score(y_train,knn_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,knn_predictions_train))
Train accuracy 0.9850686882224828
#Test accuracy:
print(classification_report(y_test,knn_predictions_test))
precision recall f1-score support
0 0.97 0.95 0.96 2042
1 0.96 0.98 0.97 2912
2 0.98 0.97 0.98 5502
3 0.98 0.98 0.98 5462
accuracy 0.98 15918
macro avg 0.97 0.97 0.97 15918
weighted avg 0.98 0.98 0.98 15918
y2=round(accuracy_score(y_test,knn_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,knn_predictions_test))
Test accuracy 0.9753737906772207
score.update({'KNN':[x2,y2]})
SVM CLASSIFIER
from sklearn.svm import SVC
param_grid = {'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']}
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)
grid.fit(X_train, y_train)
Fitting 5 folds for each of 25 candidates, totalling 125 fits [CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.974 total time= 47.1s [CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.975 total time= 41.0s [CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.976 total time= 39.7s [CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.978 total time= 42.1s [CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.974 total time= 43.2s [CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.982 total time= 14.8s [CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.982 total time= 15.0s [CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.980 total time= 14.8s [CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.983 total time= 15.2s [CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.981 total time= 14.9s [CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.973 total time= 26.4s [CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.971 total time= 26.2s [CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.972 total time= 27.0s [CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.973 total time= 27.2s [CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.972 total time= 27.1s [CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.948 total time= 58.9s [CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.945 total time= 1.0min [CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.947 total time= 58.5s [CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.945 total time= 57.8s [CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.949 total time= 57.1s [CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.767 total time= 1.8min [CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.763 total time= 1.8min [CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.766 total time= 1.8min [CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.768 total time= 1.9min [CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.761 total time= 1.8min [CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.983 total time= 28.7s [CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.984 total time= 27.3s [CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.985 total time= 26.0s [CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.986 total time= 25.6s [CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.985 total time= 25.7s [CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time= 6.9s [CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time= 6.8s [CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.991 total time= 6.8s [CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.992 total time= 6.8s [CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time= 7.0s [CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time= 11.8s [CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time= 11.9s [CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time= 11.8s [CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.990 total time= 11.9s [CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.989 total time= 12.5s [CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.974 total time= 25.8s [CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.972 total time= 27.4s [CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.973 total time= 27.6s [CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.975 total time= 30.9s [CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.974 total time= 27.6s [CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.948 total time= 1.0min [CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.945 total time= 1.0min [CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.947 total time= 59.2s [CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.946 total time= 58.9s [CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.949 total time= 1.0min [CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.986 total time= 25.2s [CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.984 total time= 24.4s [CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.984 total time= 24.0s [CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.985 total time= 25.2s [CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.987 total time= 23.2s [CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.993 total time= 3.8s [CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.994 total time= 3.9s [CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.992 total time= 4.3s [CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.994 total time= 4.4s [CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.995 total time= 4.4s [CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.994 total time= 5.9s [CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.993 total time= 6.1s [CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.993 total time= 6.5s [CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.995 total time= 6.7s [CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.995 total time= 6.6s [CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.990 total time= 12.8s [CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.988 total time= 13.3s [CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.989 total time= 14.0s [CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.991 total time= 13.6s [CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.990 total time= 12.9s [CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.974 total time= 27.8s [CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.972 total time= 26.9s [CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.973 total time= 26.8s [CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.975 total time= 26.8s [CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.974 total time= 28.6s [CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time= 27.7s [CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.983 total time= 26.5s [CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time= 26.2s [CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.986 total time= 24.5s [CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time= 22.3s [CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.994 total time= 2.3s [CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.995 total time= 2.6s [CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.993 total time= 2.3s [CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.994 total time= 2.4s [CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.996 total time= 2.3s [CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time= 3.1s [CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.997 total time= 2.9s [CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time= 3.2s [CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time= 3.0s [CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.997 total time= 3.0s [CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.995 total time= 6.0s [CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.995 total time= 5.9s [CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.994 total time= 5.6s [CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.996 total time= 5.8s [CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.996 total time= 5.7s [CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.990 total time= 11.7s [CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.988 total time= 11.5s [CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.989 total time= 11.6s [CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.991 total time= 11.7s [CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.990 total time= 12.0s [CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.985 total time= 21.7s [CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.983 total time= 21.8s [CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.984 total time= 24.3s [CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.986 total time= 22.0s [CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.984 total time= 22.2s [CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time= 2.3s [CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time= 2.3s [CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time= 2.0s [CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.995 total time= 2.0s [CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.995 total time= 2.0s [CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time= 2.0s [CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time= 2.2s [CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.996 total time= 1.9s [CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time= 1.9s [CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.998 total time= 1.9s [CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.997 total time= 3.0s [CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.997 total time= 3.1s [CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time= 3.1s [CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time= 3.1s [CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time= 3.2s [CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time= 6.3s [CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.994 total time= 6.2s [CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.994 total time= 6.0s [CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time= 6.0s [CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time= 6.1s
GridSearchCV(estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']},
verbose=3)
print(grid.best_params_)
print(grid.best_estimator_)
{'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
SVC(C=1000, gamma=0.001)
svc = SVC(C= 1000, gamma= 0.001,kernel= 'rbf')
svc.fit(X_train, y_train)
SVC_predictions_train = svc.predict(X_train)
SVC_predictions_test = svc.predict(X_test)
#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,SVC_predictions_train))
precision recall f1-score support
0 1.00 1.00 1.00 6011
1 1.00 1.00 1.00 8892
2 1.00 1.00 1.00 16220
3 1.00 1.00 1.00 16629
accuracy 1.00 47752
macro avg 1.00 1.00 1.00 47752
weighted avg 1.00 1.00 1.00 47752
x3=round(accuracy_score(y_train,SVC_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,SVC_predictions_train))
Train accuracy 0.9985340928128664
#Test accuracy:
print(classification_report(y_test,SVC_predictions_test))
precision recall f1-score support
0 1.00 1.00 1.00 2042
1 1.00 1.00 1.00 2912
2 1.00 1.00 1.00 5502
3 1.00 1.00 1.00 5462
accuracy 1.00 15918
macro avg 1.00 1.00 1.00 15918
weighted avg 1.00 1.00 1.00 15918
y3=round(accuracy_score(y_test,SVC_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,SVC_predictions_test))
Test accuracy 0.9979896971981405
score.update({'SVC':[x3,y3]})
ENSEMBLE
XG BOOST CLASSIFIER
import xgboost
from xgboost import XGBClassifier
tuning_parameters = {'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
'max_depth': range(3,10),
'gamma': [0, 1, 2, 3, 4]}
xgb_model = XGBClassifier()
xgb_grid = GridSearchCV(estimator = xgb_model, param_grid = tuning_parameters, cv = 3)
xgb_grid.fit(X_train, y_train)
print('Best parameters for XGBoost classifier: ', xgb_grid.best_params_, '\n')
[15:30:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Best parameters for XGBoost classifier: {'gamma': 0, 'learning_rate': 0.5, 'max_depth': 3}
print('Best parameters for XGBoost classifier: ', xgb_grid.best_params_, '\n')
Best parameters for XGBoost classifier: {'gamma': 0, 'learning_rate': 0.5, 'max_depth': 3}
xgb_model = XGBClassifier(max_depth = 3, gamma = 0,learning_rate=0.5)
# fit the model using fit() on train data
xgb_model.fit(X_train, y_train)
xgb_predictions_test = xgb_model.predict(X_test)
xgb_predictions_train = xgb_model.predict(X_train)
[16:51:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,xgb_predictions_train))
precision recall f1-score support
0 1.00 1.00 1.00 6011
1 1.00 1.00 1.00 8892
2 1.00 1.00 1.00 16220
3 1.00 1.00 1.00 16629
accuracy 1.00 47752
macro avg 1.00 1.00 1.00 47752
weighted avg 1.00 1.00 1.00 47752
x4=round(accuracy_score(y_train,xgb_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,xgb_predictions_train))
Train accuracy 0.9978220807505445
#Test accuracy:
print(classification_report(y_test,xgb_predictions_test))
precision recall f1-score support
0 0.99 0.98 0.98 2042
1 0.99 0.99 0.99 2912
2 0.99 0.99 0.99 5502
3 0.99 0.99 0.99 5462
accuracy 0.99 15918
macro avg 0.99 0.99 0.99 15918
weighted avg 0.99 0.99 0.99 15918
y4=round(accuracy_score(y_test,xgb_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,xgb_predictions_test))
Test accuracy 0.9905138836537254
score.update({'XGB':[x4,y4]})
score=pd.DataFrame(score).T
score
| 0 | 1 | |
|---|---|---|
| DecisionTree | 0.87 | 0.87 |
| NaiveBayes | 0.94 | 0.94 |
| KNN | 0.99 | 0.98 |
| SVC | 1.00 | 1.00 |
| XGB | 1.00 | 0.99 |
sns.lineplot(score.index,score[0],color='blue',label='Train_Score')
sns.lineplot(score.index,score[1],color='red',label='Test_Score',linestyle='--')
plt.xlabel('Classification Model')
plt.ylabel('Accuracy_Score')
plt.title('Accuracy score for Classifiers')
plt.legend()
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from matplotlib import style
from kmodes.kmodes import KModes
from kmodes.kprototypes import KPrototypes
from tqdm import tqdm
from dython.model_utils import roc_graph
from dython.nominal import associations
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv('C:/Users/bannu/Downloads/GL_Project/New folder/processed_file.csv')
df.head()
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | PersonsNights | RoomNights | DaysSinceLastStay | DaysSinceFirstStay | DistributionChannel | MarketSegment | Continent_Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0.591825 | 1.389564 | -1.261711 | 1.578742 | Corporate | Corporate | Europe |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Travel Agent/Operator | Europe |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Travel Agent/Operator | Europe |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | -0.532903 | -0.675838 | 1.584588 | 1.578742 | Travel Agent/Operator | Other | Asia |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Other | Europe |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 63670 entries, 0 to 63669 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 63670 non-null float64 1 DaysSinceCreation 63670 non-null float64 2 AverageLeadTime 63670 non-null float64 3 LodgingRevenue 63670 non-null float64 4 OtherRevenue 63670 non-null float64 5 PersonsNights 63670 non-null float64 6 RoomNights 63670 non-null float64 7 DaysSinceLastStay 63670 non-null float64 8 DaysSinceFirstStay 63670 non-null float64 9 DistributionChannel 63670 non-null object 10 MarketSegment 63670 non-null object 11 Continent_Name 63670 non-null object dtypes: float64(9), object(3) memory usage: 5.8+ MB
cost = []
for cluster in tqdm(range(1, 20)):
try:
kprototype = KPrototypes(n_jobs = -1, n_clusters = cluster, init = 'cao', n_init=15,max_iter =30)
kprototype.fit_predict(df, categorical = [9,10,11])
cost.append(kprototype.cost_)
print('Cluster initiation: {}'.format(cluster)),
except:
break
df_cost = pd.DataFrame({'Cluster':range(1, 20), 'Cost':cost})
# Data viz
plotnine.options.figure_size = (8, 4.8)
(
ggplot(data = df_cost)+
geom_line(aes(x = 'Cluster',
y = 'Cost'))+
geom_point(aes(x = 'Cluster',
y = 'Cost'))+
geom_label(aes(x = 'Cluster',
y = 'Cost',
label = 'Cluster'),
size = 10,
nudge_y = 1000) +
labs(title = 'Optimal number of cluster with Elbow Method')+
xlab('Number of Clusters k')+
ylab('Cost')+
theme_minimal()
)
<ggplot: (8784480325036)>
k_proto = KPrototypes(n_clusters= 4 , verbose= 2, max_iter = 50 , init= 'huang',n_init=15)
clusters = k_proto.fit_predict(df, categorical = [9,10,11])
Init: initializing centroids Init: initializing clusters Starting iterations... Run: 1, iteration: 1/50, moves: 16370, ncost: 332182.0527192895 Run: 1, iteration: 2/50, moves: 3371, ncost: 331071.4797125874 Run: 1, iteration: 3/50, moves: 1077, ncost: 330913.1336543894 Run: 1, iteration: 4/50, moves: 522, ncost: 330879.3844547407 Run: 1, iteration: 5/50, moves: 222, ncost: 330872.51808695274 Run: 1, iteration: 6/50, moves: 89, ncost: 330871.1669122444 Run: 1, iteration: 7/50, moves: 39, ncost: 330870.78217283415 Run: 1, iteration: 8/50, moves: 30, ncost: 330870.6229282999 Run: 1, iteration: 9/50, moves: 25, ncost: 330870.51364001964 Run: 1, iteration: 10/50, moves: 4, ncost: 330870.50747790706 Run: 1, iteration: 11/50, moves: 2, ncost: 330870.5052187324 Run: 1, iteration: 12/50, moves: 1, ncost: 330870.49822402355 Run: 1, iteration: 13/50, moves: 4, ncost: 330870.47013030096 Run: 1, iteration: 14/50, moves: 15, ncost: 330870.3954062396 Run: 1, iteration: 15/50, moves: 1, ncost: 330870.39465264813 Run: 1, iteration: 16/50, moves: 0, ncost: 330870.39465264813 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 2, iteration: 1/50, moves: 15814, ncost: 331064.94847601646 Run: 2, iteration: 2/50, moves: 4081, ncost: 329427.89422228246 Run: 2, iteration: 3/50, moves: 1906, ncost: 328931.09776377183 Run: 2, iteration: 4/50, moves: 1097, ncost: 328741.65114418307 Run: 2, iteration: 5/50, moves: 777, ncost: 328623.9628505583 Run: 2, iteration: 6/50, moves: 720, ncost: 328477.105754203 Run: 2, iteration: 7/50, moves: 729, ncost: 328338.8798468174 Run: 2, iteration: 8/50, moves: 610, ncost: 328235.66929272364 Run: 2, iteration: 9/50, moves: 603, ncost: 328124.39471500536 Run: 2, iteration: 10/50, moves: 662, ncost: 328010.3773620553 Run: 2, iteration: 11/50, moves: 431, ncost: 327974.93504220195 Run: 2, iteration: 12/50, moves: 255, ncost: 327961.9372389368 Run: 2, iteration: 13/50, moves: 114, ncost: 327958.80574702984 Run: 2, iteration: 14/50, moves: 112, ncost: 327954.72523929225 Run: 2, iteration: 15/50, moves: 84, ncost: 327952.8984636599 Run: 2, iteration: 16/50, moves: 40, ncost: 327952.58131710236 Run: 2, iteration: 17/50, moves: 19, ncost: 327952.4941192523 Run: 2, iteration: 18/50, moves: 16, ncost: 327952.4168781412 Run: 2, iteration: 19/50, moves: 19, ncost: 327952.3520741611 Run: 2, iteration: 20/50, moves: 9, ncost: 327952.3296066011 Run: 2, iteration: 21/50, moves: 1, ncost: 327952.3292628051 Run: 2, iteration: 22/50, moves: 0, ncost: 327952.3292628051 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 3, iteration: 1/50, moves: 29377, ncost: 346089.2256848074 Run: 3, iteration: 2/50, moves: 10036, ncost: 333959.5950330606 Run: 3, iteration: 3/50, moves: 2128, ncost: 333500.1599934982 Run: 3, iteration: 4/50, moves: 1026, ncost: 333389.5348186114 Run: 3, iteration: 5/50, moves: 541, ncost: 333357.6991360698 Run: 3, iteration: 6/50, moves: 286, ncost: 333348.1791672376 Run: 3, iteration: 7/50, moves: 151, ncost: 333345.1417213754 Run: 3, iteration: 8/50, moves: 110, ncost: 333343.01122948 Run: 3, iteration: 9/50, moves: 93, ncost: 333338.0483779666 Run: 3, iteration: 10/50, moves: 231, ncost: 333321.2509086866 Run: 3, iteration: 11/50, moves: 254, ncost: 333300.1169902591 Run: 3, iteration: 12/50, moves: 299, ncost: 333273.5507613123 Run: 3, iteration: 13/50, moves: 336, ncost: 333234.8901824862 Run: 3, iteration: 14/50, moves: 421, ncost: 333176.76189495827 Run: 3, iteration: 15/50, moves: 627, ncost: 333057.14972587 Run: 3, iteration: 16/50, moves: 638, ncost: 332927.2989999464 Run: 3, iteration: 17/50, moves: 680, ncost: 332756.91911392036 Run: 3, iteration: 18/50, moves: 778, ncost: 332544.50876860146 Run: 3, iteration: 19/50, moves: 831, ncost: 332276.9385628156 Run: 3, iteration: 20/50, moves: 881, ncost: 332000.02574535285 Run: 3, iteration: 21/50, moves: 820, ncost: 331723.068837588 Run: 3, iteration: 22/50, moves: 937, ncost: 331393.2679034874 Run: 3, iteration: 23/50, moves: 767, ncost: 331180.13661276794 Run: 3, iteration: 24/50, moves: 614, ncost: 331027.44595540885 Run: 3, iteration: 25/50, moves: 509, ncost: 330935.5843401023 Run: 3, iteration: 26/50, moves: 372, ncost: 330898.1448469224 Run: 3, iteration: 27/50, moves: 265, ncost: 330881.0053101479 Run: 3, iteration: 28/50, moves: 133, ncost: 330876.52173072007 Run: 3, iteration: 29/50, moves: 79, ncost: 330873.67414245754 Run: 3, iteration: 30/50, moves: 74, ncost: 330871.30987174256 Run: 3, iteration: 31/50, moves: 39, ncost: 330870.7828011773 Run: 3, iteration: 32/50, moves: 23, ncost: 330870.55462097184 Run: 3, iteration: 33/50, moves: 13, ncost: 330870.47374855785 Run: 3, iteration: 34/50, moves: 9, ncost: 330870.437255312 Run: 3, iteration: 35/50, moves: 2, ncost: 330870.43449498026 Run: 3, iteration: 36/50, moves: 2, ncost: 330870.43215123384 Run: 3, iteration: 37/50, moves: 3, ncost: 330870.42450064543 Run: 3, iteration: 38/50, moves: 5, ncost: 330870.4095156377 Run: 3, iteration: 39/50, moves: 3, ncost: 330870.40161713306 Run: 3, iteration: 40/50, moves: 1, ncost: 330870.40010763047 Run: 3, iteration: 41/50, moves: 0, ncost: 330870.40010763047 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 4, iteration: 1/50, moves: 18893, ncost: 339561.7348435977 Run: 4, iteration: 2/50, moves: 6722, ncost: 331918.1693104725 Run: 4, iteration: 3/50, moves: 4032, ncost: 329294.31514153234 Run: 4, iteration: 4/50, moves: 2148, ncost: 328610.0485875048 Run: 4, iteration: 5/50, moves: 1426, ncost: 328262.1404197179 Run: 4, iteration: 6/50, moves: 924, ncost: 328119.9148017382 Run: 4, iteration: 7/50, moves: 653, ncost: 328045.76471554174 Run: 4, iteration: 8/50, moves: 556, ncost: 327990.67354892363 Run: 4, iteration: 9/50, moves: 438, ncost: 327961.6268966945 Run: 4, iteration: 10/50, moves: 215, ncost: 327955.289753461 Run: 4, iteration: 11/50, moves: 103, ncost: 327953.88643309264 Run: 4, iteration: 12/50, moves: 64, ncost: 327953.3087166805 Run: 4, iteration: 13/50, moves: 45, ncost: 327952.9713238563 Run: 4, iteration: 14/50, moves: 29, ncost: 327952.80561984546 Run: 4, iteration: 15/50, moves: 25, ncost: 327952.63398581825 Run: 4, iteration: 16/50, moves: 30, ncost: 327952.4108709016 Run: 4, iteration: 17/50, moves: 14, ncost: 327952.3693699302 Run: 4, iteration: 18/50, moves: 6, ncost: 327952.35642089375 Run: 4, iteration: 19/50, moves: 2, ncost: 327952.349775432 Run: 4, iteration: 20/50, moves: 1, ncost: 327952.34722393064 Run: 4, iteration: 21/50, moves: 0, ncost: 327952.34722393064 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 5, iteration: 1/50, moves: 16862, ncost: 332029.7642745474 Run: 5, iteration: 2/50, moves: 4388, ncost: 330103.97621850815 Run: 5, iteration: 3/50, moves: 2448, ncost: 329379.907129429 Run: 5, iteration: 4/50, moves: 1702, ncost: 328971.6340607473 Run: 5, iteration: 5/50, moves: 1309, ncost: 328712.31935572496 Run: 5, iteration: 6/50, moves: 1071, ncost: 328520.70991536166 Run: 5, iteration: 7/50, moves: 871, ncost: 328350.2649682622 Run: 5, iteration: 8/50, moves: 782, ncost: 328222.9740850017 Run: 5, iteration: 9/50, moves: 666, ncost: 328135.96260721196 Run: 5, iteration: 10/50, moves: 565, ncost: 328072.7366671492 Run: 5, iteration: 11/50, moves: 501, ncost: 328019.2398626853 Run: 5, iteration: 12/50, moves: 429, ncost: 327985.4892262595 Run: 5, iteration: 13/50, moves: 348, ncost: 327966.87230989593 Run: 5, iteration: 14/50, moves: 201, ncost: 327960.01242790703 Run: 5, iteration: 15/50, moves: 158, ncost: 327955.41377822496 Run: 5, iteration: 16/50, moves: 100, ncost: 327953.9809322712 Run: 5, iteration: 17/50, moves: 62, ncost: 327953.3758901359 Run: 5, iteration: 18/50, moves: 46, ncost: 327953.0387118296 Run: 5, iteration: 19/50, moves: 30, ncost: 327952.8397272576 Run: 5, iteration: 20/50, moves: 19, ncost: 327952.7253174698 Run: 5, iteration: 21/50, moves: 25, ncost: 327952.5268988979 Run: 5, iteration: 22/50, moves: 24, ncost: 327952.387522905 Run: 5, iteration: 23/50, moves: 8, ncost: 327952.3693699297 Run: 5, iteration: 24/50, moves: 6, ncost: 327952.35642089316 Run: 5, iteration: 25/50, moves: 2, ncost: 327952.3497754313 Run: 5, iteration: 26/50, moves: 1, ncost: 327952.3472239298 Run: 5, iteration: 27/50, moves: 0, ncost: 327952.3472239298 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 6, iteration: 1/50, moves: 19984, ncost: 344915.3462015841 Run: 6, iteration: 2/50, moves: 8609, ncost: 333744.3495210195 Run: 6, iteration: 3/50, moves: 5675, ncost: 329893.3370119469 Run: 6, iteration: 4/50, moves: 2997, ncost: 328817.75813484774 Run: 6, iteration: 5/50, moves: 1343, ncost: 328542.8811673044 Run: 6, iteration: 6/50, moves: 847, ncost: 328408.26198056637 Run: 6, iteration: 7/50, moves: 689, ncost: 328309.00151370664 Run: 6, iteration: 8/50, moves: 689, ncost: 328173.0991784212 Run: 6, iteration: 9/50, moves: 673, ncost: 328050.8414886471 Run: 6, iteration: 10/50, moves: 590, ncost: 327981.7494027284 Run: 6, iteration: 11/50, moves: 342, ncost: 327963.6652303224 Run: 6, iteration: 12/50, moves: 161, ncost: 327959.06233444466 Run: 6, iteration: 13/50, moves: 117, ncost: 327954.86332033377 Run: 6, iteration: 14/50, moves: 93, ncost: 327952.8874969845 Run: 6, iteration: 15/50, moves: 41, ncost: 327952.5726867776 Run: 6, iteration: 16/50, moves: 16, ncost: 327952.49626272 Run: 6, iteration: 17/50, moves: 20, ncost: 327952.4008624499 Run: 6, iteration: 18/50, moves: 14, ncost: 327952.3518192695 Run: 6, iteration: 19/50, moves: 9, ncost: 327952.3296066009 Run: 6, iteration: 20/50, moves: 1, ncost: 327952.3292628055 Run: 6, iteration: 21/50, moves: 0, ncost: 327952.3292628055 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 7, iteration: 1/50, moves: 28735, ncost: 342971.77590722975 Run: 7, iteration: 2/50, moves: 6647, ncost: 334240.58415789483 Run: 7, iteration: 3/50, moves: 3810, ncost: 330992.20057965384 Run: 7, iteration: 4/50, moves: 2653, ncost: 329682.2964339171 Run: 7, iteration: 5/50, moves: 1911, ncost: 329130.45079138706 Run: 7, iteration: 6/50, moves: 1475, ncost: 328808.50976640085 Run: 7, iteration: 7/50, moves: 1131, ncost: 328603.18836461176 Run: 7, iteration: 8/50, moves: 928, ncost: 328428.64055978134 Run: 7, iteration: 9/50, moves: 826, ncost: 328276.49032562145 Run: 7, iteration: 10/50, moves: 720, ncost: 328172.22866986913 Run: 7, iteration: 11/50, moves: 621, ncost: 328099.95636101894 Run: 7, iteration: 12/50, moves: 590, ncost: 328034.5059732224 Run: 7, iteration: 13/50, moves: 476, ncost: 327994.01820132526 Run: 7, iteration: 14/50, moves: 388, ncost: 327969.71621679846 Run: 7, iteration: 15/50, moves: 239, ncost: 327961.0991394548 Run: 7, iteration: 16/50, moves: 161, ncost: 327955.952526057 Run: 7, iteration: 17/50, moves: 99, ncost: 327954.3280972795 Run: 7, iteration: 18/50, moves: 75, ncost: 327953.5001150774 Run: 7, iteration: 19/50, moves: 49, ncost: 327953.1251397196 Run: 7, iteration: 20/50, moves: 35, ncost: 327952.892054883 Run: 7, iteration: 21/50, moves: 24, ncost: 327952.7514403655 Run: 7, iteration: 22/50, moves: 27, ncost: 327952.5433267152 Run: 7, iteration: 23/50, moves: 27, ncost: 327952.3875229048 Run: 7, iteration: 24/50, moves: 8, ncost: 327952.3693699304 Run: 7, iteration: 25/50, moves: 6, ncost: 327952.35642089247 Run: 7, iteration: 26/50, moves: 2, ncost: 327952.3497754312 Run: 7, iteration: 27/50, moves: 1, ncost: 327952.3472239296 Run: 7, iteration: 28/50, moves: 0, ncost: 327952.3472239296 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 8, iteration: 1/50, moves: 30097, ncost: 337828.496707534 Run: 8, iteration: 2/50, moves: 5818, ncost: 332942.32566174114 Run: 8, iteration: 3/50, moves: 3004, ncost: 331477.82941394165 Run: 8, iteration: 4/50, moves: 1610, ncost: 331033.6603364823 Run: 8, iteration: 5/50, moves: 826, ncost: 330920.476950288 Run: 8, iteration: 6/50, moves: 428, ncost: 330886.97299437446 Run: 8, iteration: 7/50, moves: 252, ncost: 330875.9173645625 Run: 8, iteration: 8/50, moves: 146, ncost: 330871.6425072911 Run: 8, iteration: 9/50, moves: 59, ncost: 330870.92695826385 Run: 8, iteration: 10/50, moves: 33, ncost: 330870.65084336186 Run: 8, iteration: 11/50, moves: 17, ncost: 330870.5577676345 Run: 8, iteration: 12/50, moves: 12, ncost: 330870.51754644816 Run: 8, iteration: 13/50, moves: 11, ncost: 330870.4652009683 Run: 8, iteration: 14/50, moves: 7, ncost: 330870.4287670057 Run: 8, iteration: 15/50, moves: 6, ncost: 330870.4025289204 Run: 8, iteration: 16/50, moves: 1, ncost: 330870.4007236011 Run: 8, iteration: 17/50, moves: 0, ncost: 330870.4007236011 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 9, iteration: 1/50, moves: 32567, ncost: 342557.4139099887 Run: 9, iteration: 2/50, moves: 6081, ncost: 337925.28796494176 Run: 9, iteration: 3/50, moves: 4068, ncost: 334951.3046702748 Run: 9, iteration: 4/50, moves: 2905, ncost: 333622.6110086613 Run: 9, iteration: 5/50, moves: 1524, ncost: 333306.5998919689 Run: 9, iteration: 6/50, moves: 839, ncost: 333195.82556226244 Run: 9, iteration: 7/50, moves: 709, ncost: 333072.69187604677 Run: 9, iteration: 8/50, moves: 663, ncost: 332942.76158289105 Run: 9, iteration: 9/50, moves: 685, ncost: 332774.8004421763 Run: 9, iteration: 10/50, moves: 781, ncost: 332566.13232252805 Run: 9, iteration: 11/50, moves: 831, ncost: 332300.1087750088 Run: 9, iteration: 12/50, moves: 886, ncost: 332016.8133406674 Run: 9, iteration: 13/50, moves: 829, ncost: 331739.95998503023 Run: 9, iteration: 14/50, moves: 931, ncost: 331410.7072878081 Run: 9, iteration: 15/50, moves: 780, ncost: 331192.58094839985 Run: 9, iteration: 16/50, moves: 642, ncost: 331031.11429016455 Run: 9, iteration: 17/50, moves: 522, ncost: 330936.21911703446 Run: 9, iteration: 18/50, moves: 371, ncost: 330898.4222693686 Run: 9, iteration: 19/50, moves: 268, ncost: 330881.1395190745 Run: 9, iteration: 20/50, moves: 137, ncost: 330876.5516950981 Run: 9, iteration: 21/50, moves: 83, ncost: 330873.67414245795 Run: 9, iteration: 22/50, moves: 74, ncost: 330871.30987174227 Run: 9, iteration: 23/50, moves: 39, ncost: 330870.7828011774 Run: 9, iteration: 24/50, moves: 23, ncost: 330870.5546209703 Run: 9, iteration: 25/50, moves: 13, ncost: 330870.47374855727 Run: 9, iteration: 26/50, moves: 9, ncost: 330870.43725531205 Run: 9, iteration: 27/50, moves: 2, ncost: 330870.43449498015 Run: 9, iteration: 28/50, moves: 2, ncost: 330870.43215123453 Run: 9, iteration: 29/50, moves: 3, ncost: 330870.4245006456 Run: 9, iteration: 30/50, moves: 5, ncost: 330870.40951563756 Run: 9, iteration: 31/50, moves: 3, ncost: 330870.40161713294 Run: 9, iteration: 32/50, moves: 1, ncost: 330870.4001076307 Run: 9, iteration: 33/50, moves: 0, ncost: 330870.4001076307 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 10, iteration: 1/50, moves: 13817, ncost: 343838.4401145477 Run: 10, iteration: 2/50, moves: 7561, ncost: 334372.1888281921 Run: 10, iteration: 3/50, moves: 4607, ncost: 331898.8560813422 Run: 10, iteration: 4/50, moves: 2107, ncost: 331300.6710739151 Run: 10, iteration: 5/50, moves: 1226, ncost: 330932.0323791534 Run: 10, iteration: 6/50, moves: 1068, ncost: 330517.05696105247 Run: 10, iteration: 7/50, moves: 1197, ncost: 330114.5373901234 Run: 10, iteration: 8/50, moves: 1170, ncost: 329839.1889918673 Run: 10, iteration: 9/50, moves: 971, ncost: 329670.20317120117 Run: 10, iteration: 10/50, moves: 753, ncost: 329565.3017385462 Run: 10, iteration: 11/50, moves: 579, ncost: 329510.0091609578 Run: 10, iteration: 12/50, moves: 405, ncost: 329486.80281079723 Run: 10, iteration: 13/50, moves: 268, ncost: 329475.07968835154 Run: 10, iteration: 14/50, moves: 264, ncost: 329462.2927811659 Run: 10, iteration: 15/50, moves: 359, ncost: 329443.9355907085 Run: 10, iteration: 16/50, moves: 286, ncost: 329432.6193410474 Run: 10, iteration: 17/50, moves: 229, ncost: 329425.09594674216 Run: 10, iteration: 18/50, moves: 158, ncost: 329421.0964490192 Run: 10, iteration: 19/50, moves: 120, ncost: 329418.48573775013 Run: 10, iteration: 20/50, moves: 94, ncost: 329417.131912286 Run: 10, iteration: 21/50, moves: 52, ncost: 329416.74004029075 Run: 10, iteration: 22/50, moves: 45, ncost: 329416.26802756696 Run: 10, iteration: 23/50, moves: 59, ncost: 329415.53249781934 Run: 10, iteration: 24/50, moves: 59, ncost: 329414.90654766967 Run: 10, iteration: 25/50, moves: 67, ncost: 329414.047668261 Run: 10, iteration: 26/50, moves: 66, ncost: 329413.2654348498 Run: 10, iteration: 27/50, moves: 57, ncost: 329412.7101748951 Run: 10, iteration: 28/50, moves: 94, ncost: 329410.6112939277 Run: 10, iteration: 29/50, moves: 117, ncost: 329408.463534639 Run: 10, iteration: 30/50, moves: 73, ncost: 329407.61453865515 Run: 10, iteration: 31/50, moves: 45, ncost: 329407.22322276665 Run: 10, iteration: 32/50, moves: 32, ncost: 329407.0334538882 Run: 10, iteration: 33/50, moves: 40, ncost: 329406.8308592081 Run: 10, iteration: 34/50, moves: 18, ncost: 329406.7813625913 Run: 10, iteration: 35/50, moves: 4, ncost: 329406.7756372554 Run: 10, iteration: 36/50, moves: 0, ncost: 329406.7756372554 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 11, iteration: 1/50, moves: 32606, ncost: 338019.6824556815 Run: 11, iteration: 2/50, moves: 6681, ncost: 333326.6337246361 Run: 11, iteration: 3/50, moves: 3065, ncost: 331757.11856943776 Run: 11, iteration: 4/50, moves: 2121, ncost: 330993.2385649948 Run: 11, iteration: 5/50, moves: 1688, ncost: 330456.28692060855 Run: 11, iteration: 6/50, moves: 1347, ncost: 330078.66284686973 Run: 11, iteration: 7/50, moves: 1156, ncost: 329832.1164140871 Run: 11, iteration: 8/50, moves: 976, ncost: 329668.233681285 Run: 11, iteration: 9/50, moves: 757, ncost: 329566.10229903134 Run: 11, iteration: 10/50, moves: 580, ncost: 329511.01567630953 Run: 11, iteration: 11/50, moves: 414, ncost: 329487.1109180923 Run: 11, iteration: 12/50, moves: 271, ncost: 329475.228121823 Run: 11, iteration: 13/50, moves: 266, ncost: 329462.3529683459 Run: 11, iteration: 14/50, moves: 360, ncost: 329443.93559070927 Run: 11, iteration: 15/50, moves: 286, ncost: 329432.619341048 Run: 11, iteration: 16/50, moves: 229, ncost: 329425.095946743 Run: 11, iteration: 17/50, moves: 158, ncost: 329421.09644901875 Run: 11, iteration: 18/50, moves: 120, ncost: 329418.4857377501 Run: 11, iteration: 19/50, moves: 94, ncost: 329417.13191228564 Run: 11, iteration: 20/50, moves: 52, ncost: 329416.74004029087 Run: 11, iteration: 21/50, moves: 45, ncost: 329416.26802756725 Run: 11, iteration: 22/50, moves: 59, ncost: 329415.5324978194 Run: 11, iteration: 23/50, moves: 59, ncost: 329414.90654766955 Run: 11, iteration: 24/50, moves: 67, ncost: 329414.0476682617 Run: 11, iteration: 25/50, moves: 66, ncost: 329413.26543484896 Run: 11, iteration: 26/50, moves: 57, ncost: 329412.71017489495 Run: 11, iteration: 27/50, moves: 94, ncost: 329410.611293928 Run: 11, iteration: 28/50, moves: 117, ncost: 329408.46353463916 Run: 11, iteration: 29/50, moves: 73, ncost: 329407.6145386551 Run: 11, iteration: 30/50, moves: 45, ncost: 329407.2232227668 Run: 11, iteration: 31/50, moves: 32, ncost: 329407.03345388797 Run: 11, iteration: 32/50, moves: 40, ncost: 329406.83085920813 Run: 11, iteration: 33/50, moves: 18, ncost: 329406.7813625913 Run: 11, iteration: 34/50, moves: 4, ncost: 329406.7756372551 Run: 11, iteration: 35/50, moves: 0, ncost: 329406.7756372551 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 12, iteration: 1/50, moves: 13255, ncost: 333941.76490359666 Run: 12, iteration: 2/50, moves: 4626, ncost: 332011.0607974965 Run: 12, iteration: 3/50, moves: 2034, ncost: 331224.1695276123 Run: 12, iteration: 4/50, moves: 1267, ncost: 330986.26335662726 Run: 12, iteration: 5/50, moves: 683, ncost: 330909.03691375256 Run: 12, iteration: 6/50, moves: 396, ncost: 330882.9698964935 Run: 12, iteration: 7/50, moves: 234, ncost: 330873.987839557 Run: 12, iteration: 8/50, moves: 114, ncost: 330871.2693233985 Run: 12, iteration: 9/50, moves: 48, ncost: 330870.7428313399 Run: 12, iteration: 10/50, moves: 26, ncost: 330870.57537243323 Run: 12, iteration: 11/50, moves: 16, ncost: 330870.5214017958 Run: 12, iteration: 12/50, moves: 11, ncost: 330870.4655271171 Run: 12, iteration: 13/50, moves: 8, ncost: 330870.42876700533 Run: 12, iteration: 14/50, moves: 6, ncost: 330870.4025289203 Run: 12, iteration: 15/50, moves: 1, ncost: 330870.40072360105 Run: 12, iteration: 16/50, moves: 0, ncost: 330870.40072360105 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 13, iteration: 1/50, moves: 22065, ncost: 337516.8248952934 Run: 13, iteration: 2/50, moves: 5748, ncost: 332218.473318809 Run: 13, iteration: 3/50, moves: 2455, ncost: 331173.95442265953 Run: 13, iteration: 4/50, moves: 1017, ncost: 330945.2509053001 Run: 13, iteration: 5/50, moves: 496, ncost: 330890.6217795961 Run: 13, iteration: 6/50, moves: 252, ncost: 330877.55850255623 Run: 13, iteration: 7/50, moves: 126, ncost: 330873.1671523429 Run: 13, iteration: 8/50, moves: 75, ncost: 330871.2104535868 Run: 13, iteration: 9/50, moves: 42, ncost: 330870.71343891596 Run: 13, iteration: 10/50, moves: 25, ncost: 330870.52711509774 Run: 13, iteration: 11/50, moves: 18, ncost: 330870.4438646221 Run: 13, iteration: 12/50, moves: 7, ncost: 330870.43199364643 Run: 13, iteration: 13/50, moves: 5, ncost: 330870.42354953074 Run: 13, iteration: 14/50, moves: 0, ncost: 330870.42354953074 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 14, iteration: 1/50, moves: 27818, ncost: 346091.39717139787 Run: 14, iteration: 2/50, moves: 6766, ncost: 340994.7008944515 Run: 14, iteration: 3/50, moves: 3409, ncost: 339042.3321360135 Run: 14, iteration: 4/50, moves: 3060, ncost: 337695.9868959047 Run: 14, iteration: 5/50, moves: 3605, ncost: 335502.5293387175 Run: 14, iteration: 6/50, moves: 3581, ncost: 333634.6769040707 Run: 14, iteration: 7/50, moves: 3040, ncost: 332098.6893820456 Run: 14, iteration: 8/50, moves: 2350, ncost: 331235.3819110435 Run: 14, iteration: 9/50, moves: 1726, ncost: 330713.5156592098 Run: 14, iteration: 10/50, moves: 1384, ncost: 330271.0993506366 Run: 14, iteration: 11/50, moves: 1265, ncost: 329961.7607449689 Run: 14, iteration: 12/50, moves: 1094, ncost: 329751.55353500554 Run: 14, iteration: 13/50, moves: 891, ncost: 329616.2909962701 Run: 14, iteration: 14/50, moves: 729, ncost: 329530.7126512566 Run: 14, iteration: 15/50, moves: 515, ncost: 329496.28148689296 Run: 14, iteration: 16/50, moves: 319, ncost: 329479.70318719785 Run: 14, iteration: 17/50, moves: 247, ncost: 329469.48111102125 Run: 14, iteration: 18/50, moves: 296, ncost: 329453.7742080854 Run: 14, iteration: 19/50, moves: 298, ncost: 329439.2411591536 Run: 14, iteration: 20/50, moves: 270, ncost: 329429.14760876127 Run: 14, iteration: 21/50, moves: 200, ncost: 329423.26265908324 Run: 14, iteration: 22/50, moves: 140, ncost: 329420.01355852647 Run: 14, iteration: 23/50, moves: 106, ncost: 329417.9091370084 Run: 14, iteration: 24/50, moves: 81, ncost: 329416.96074809914 Run: 14, iteration: 25/50, moves: 47, ncost: 329416.6261214159 Run: 14, iteration: 26/50, moves: 57, ncost: 329415.9226996333 Run: 14, iteration: 27/50, moves: 54, ncost: 329415.2902361125 Run: 14, iteration: 28/50, moves: 61, ncost: 329414.6351341191 Run: 14, iteration: 29/50, moves: 78, ncost: 329413.6307400134 Run: 14, iteration: 30/50, moves: 67, ncost: 329412.92229496565 Run: 14, iteration: 31/50, moves: 50, ncost: 329412.43167294975 Run: 14, iteration: 32/50, moves: 118, ncost: 329409.5008635875 Run: 14, iteration: 33/50, moves: 94, ncost: 329408.07712416735 Run: 14, iteration: 34/50, moves: 54, ncost: 329407.54439742956 Run: 14, iteration: 35/50, moves: 45, ncost: 329407.15138154564 Run: 14, iteration: 36/50, moves: 41, ncost: 329406.9401624331 Run: 14, iteration: 37/50, moves: 33, ncost: 329406.7913684379 Run: 14, iteration: 38/50, moves: 9, ncost: 329406.776793438 Run: 14, iteration: 39/50, moves: 0, ncost: 329406.776793438 Init: initializing centroids Init: initializing clusters Starting iterations... Run: 15, iteration: 1/50, moves: 15373, ncost: 360790.2971112735 Run: 15, iteration: 2/50, moves: 6879, ncost: 356097.04691125295 Run: 15, iteration: 3/50, moves: 4116, ncost: 352034.20162928314 Run: 15, iteration: 4/50, moves: 4687, ncost: 346182.51807123586 Run: 15, iteration: 5/50, moves: 4759, ncost: 339551.6079500333 Run: 15, iteration: 6/50, moves: 5044, ncost: 334145.25598358613 Run: 15, iteration: 7/50, moves: 3233, ncost: 331955.57934890833 Run: 15, iteration: 8/50, moves: 1986, ncost: 331174.90519809036 Run: 15, iteration: 9/50, moves: 1031, ncost: 330961.6422965072 Run: 15, iteration: 10/50, moves: 566, ncost: 330896.1132909785 Run: 15, iteration: 11/50, moves: 318, ncost: 330877.4115637081 Run: 15, iteration: 12/50, moves: 167, ncost: 330871.8621011546 Run: 15, iteration: 13/50, moves: 63, ncost: 330870.79051164014 Run: 15, iteration: 14/50, moves: 24, ncost: 330870.63117846864 Run: 15, iteration: 15/50, moves: 14, ncost: 330870.5446469182 Run: 15, iteration: 16/50, moves: 23, ncost: 330870.41957646795 Run: 15, iteration: 17/50, moves: 9, ncost: 330870.37586539995 Run: 15, iteration: 18/50, moves: 5, ncost: 330870.365884452 Run: 15, iteration: 19/50, moves: 1, ncost: 330870.3650780018 Run: 15, iteration: 20/50, moves: 0, ncost: 330870.3650780018 Best run was number 2
# Print cluster centroids of the trained model.
print(k_proto.cluster_centroids_)
[['-0.026474219423090733' '0.9270988108485877' '-0.23202513323310273' '-0.6485379695241148' '-0.49503928066593206' '-0.636223914836607' '-0.6619409334218479' '0.9264933475219304' '0.9238721302369118' 'Travel Agent/Operator' 'Other' 'Europe'] ['0.19655852227956094' '0.7485119297437397' '0.27666755735969256' '0.4245679362905632' '0.6920935420081291' '0.8321333052517851' '0.7816457156368039' '0.7461077330740551' '0.7524292586507885' 'Travel Agent/Operator' 'Other' 'Europe'] ['-0.11813409773831288' '-0.833980898532122' '0.13222324007531272' '1.3292120368016815' '0.8887509164296602' '1.0815161438956529' '1.1191601051855908' '-0.8357187970010412' '-0.82800699069321' 'Travel Agent/Operator' 'Other' 'Europe'] ['-0.04729913705966276' '-0.8740579230374662' '-0.04769458925786531' '-0.3830663690531079' '-0.4712116309702116' '-0.5376728589263355' '-0.5011518088636748' '-0.8710910775435204' '-0.8768297963777862' 'Travel Agent/Operator' 'Other' 'Europe']]
# Print training statistics
print(k_proto.cost_)
print(k_proto.n_iter_)
327952.3292628051 22
#Print count of each cluster
print(pd.Series(clusters).value_counts())
3 21105 0 18182 1 13855 2 10528 dtype: int64
proto_labs = k_proto.labels_
proto_labs
array([0, 1, 1, ..., 0, 0, 0], dtype=uint16)
cluster_dict = []
for c in clusters:
cluster_dict.append(c)
df.head()
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | PersonsNights | RoomNights | DaysSinceLastStay | DaysSinceFirstStay | DistributionChannel | MarketSegment | Continent_Name | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0.591825 | 1.389564 | -1.261711 | 1.578742 | Corporate | Corporate | Europe | 2 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Travel Agent/Operator | Europe | 1 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Travel Agent/Operator | Europe | 1 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | -0.532903 | -0.675838 | 1.584588 | 1.578742 | Travel Agent/Operator | Other | Asia | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 1.154189 | 1.389564 | 1.584588 | 1.578742 | Travel Agent/Operator | Other | Europe | 1 |
df['cluster'] = cluster_dict
plt.subplots(figsize = (10,6))
sns.countplot(x=df['MarketSegment'],order=df['MarketSegment'].value_counts().index,hue=df['cluster'])
plt.show()
plt.subplots(figsize = (10,6))
sns.countplot(x=df['Continent_Name'],order=df['Continent_Name'].value_counts().index,hue=df['cluster'])
plt.show()
plt.subplots(figsize = (10,6))
sns.countplot(x=df['DistributionChannel'],order=df['DistributionChannel'].value_counts().index,hue=df['cluster'])
plt.show()
import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
plt.style.use('seaborn')
data=pd.read_csv('processed_data.csv')
data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
'DaysSinceFirstStay']]
import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
plt.style.use('seaborn')
data=pd.read_csv('processed_data.csv')
data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
'DaysSinceFirstStay']]
# Dimensionality reduction
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture
# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)
explained_variance = pca.explained_variance_
# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()
pca.explained_variance_ratio_
array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
3.44155412e-05])
#Scree plot
import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))
num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100
ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)
for i in range(9):
ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))
ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')
ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)
(-1.624643895711861, 37.56515055083923)
pca = PCA().fit(data1)
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)
fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')
plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '90% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()
[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055 0.99891343 0.99996558 1. ]
pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)
# BIC for GMM
from sklearn.mixture import GaussianMixture
n_components = range(1, 30)
covariance_type = [ 'spherical', 'tied', 'diag', 'full']
spherical_score=[]
tied_score=[]
diag_score=[]
full_score=[]
for cov in covariance_type:
for n_comp in n_components:
gmm=GaussianMixture(n_components=n_comp,covariance_type=cov)
gmm.fit(x_7cols)
if cov=='spherical':
spherical_score.append((gmm.bic(x_7cols)))
elif cov=='tied':
tied_score.append((gmm.bic(x_7cols)))
elif cov=='diag':
diag_score.append((gmm.bic(x_7cols)))
elif cov=='full':
full_score.append((gmm.bic(x_7cols)))
x=list(range(1,30))
fig, axs = plt.subplots(2,2,figsize=(10,10))
axs[0,0].plot(x,spherical_score,color='red')
axs[0,0].title.set_text('cov_type :Spherical')
axs[0,1].plot(x,tied_score,color='blue')
axs[0,1].title.set_text('cov_type : Tied')
axs[1,0].plot(x,diag_score,color='green')
axs[1,0].title.set_text('cov_type :Diag')
axs[1,1].plot(x,full_score,color='black')
axs[1,1].title.set_text('cov_type : Full')
fig.suptitle('BIC score', fontsize=14)
#plt.legend()
plt.show()
n_components = np.arange(1, 10)
clfs = [GaussianMixture(n, max_iter = 1000).fit(x_7cols) for n in n_components]
bics = [clf.bic(x_7cols) for clf in clfs]
aics = [clf.aic(x_7cols) for clf in clfs]
fig,axs=plt.subplots(1,2,figsize=(10,6))
axs[0].plot(n_components, bics, label = 'BIC',color='red')
axs[0].set_xlabel('n_components')
axs[0].set_ylabel('BIC SCORE')
axs[1].plot(n_components, aics, label = 'AIC')
axs[1].set_xlabel('n_components')
axs[1].set_ylabel('AIC SCORE')
fig.suptitle(' AIC & BIC score', fontsize=14)
plt.legend()
plt.show()
# Initializes and predicts labels
gm_labels = GaussianMixture(n_components=4, covariance_type='full').fit_predict(x_7cols)
# Calculates average silhouette coefficient
silhouette_gm = silhouette_score(x_7cols, gm_labels)
# Creates dataframe of the labels for later use
gaussmix_df = pd.DataFrame(gm_labels, columns=['cluster_id'])
gaussmix_df['cluster_id'].unique()
array([1, 3, 0, 2], dtype=int64)
print('The Silhouette coefficient for the EM Clustering algorithm is '+str(round(silhouette_gm, 2)))
# Creates plot of cluster sizes
gaussmix_df.value_counts().sort_index().plot.bar(figsize=(7, 6))
plt.title('Expectation Maximization clusters size distribution', fontsize=14)
plt.xlabel('Cluster', fontsize=13)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','k 4'], rotation=0)
plt.show()
The Silhouette coefficient for the EM Clustering algorithm is 0.09
df_k = data1.copy(deep=True)
df_k['label'] = gm_labels
df_k
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | PersonsNights | RoomNights | DaysSinceLastStay | DaysSinceFirstStay | label | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 4 | 2 | -1.261711 | 1.578742 | 1 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 4 | 2 | 1.584588 | 1.578742 | 3 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 4 | 2 | 1.584588 | 1.578742 | 3 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | 3 | 1 | 1.584588 | 1.578742 | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 4 | 2 | 1.584588 | 1.578742 | 3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 63665 | 0.041387 | -1.547764 | -0.733896 | -0.778844 | 0.944011 | 3 | 1 | -1.543988 | -1.548269 | 0 |
| 63666 | 0.115001 | -1.547764 | 0.044904 | -0.914618 | -1.030075 | 2 | 0 | -1.543988 | -1.548269 | 0 |
| 63667 | 0.335844 | -1.547764 | 0.319129 | -0.116844 | 0.362986 | 4 | 2 | -1.543988 | -1.548269 | 3 |
| 63668 | -0.032227 | -1.547764 | 0.242346 | -0.157165 | 2.666086 | 4 | 2 | -1.543988 | -1.548269 | 3 |
| 63669 | 1.808129 | -1.547764 | 0.242346 | 2.534449 | -0.988073 | 4 | 2 | -1.543988 | -1.548269 | 3 |
63670 rows × 10 columns
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df_k['AverageLeadTime'],df_k['LodgingRevenue'],hue=df_k['label'],palette="tab10")
plt.legend()
plt.show()
from sklearn import metrics
from sklearn.metrics import davies_bouldin_score
#SILHOUETTE SCORE
metrics.silhouette_score(x_7cols, gm_labels, metric='euclidean')
0.08758197037444428
#Calinski-Harabasz Index
metrics.calinski_harabasz_score(x_7cols, gm_labels)
6031.089723494116
#Davies-Bouldin Index
#AFTER PCA
davies_bouldin_score(x_7cols, gm_labels)
2.6315988821797385
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import matplotlib
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA
# Importing the dataset
X1 = pd.read_csv("processed_data.csv")
X1.head()
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | BookingsCanceled | BookingsNoShowed | BookingsCheckedIn | PersonsNights | RoomNights | ... | MarketSegment_Direct | MarketSegment_Groups | MarketSegment_Other | MarketSegment_Travel Agent/Operator | Continent_Name_Antarctica | Continent_Name_Asia | Continent_Name_Europe | Continent_Name_North America | Continent_Name_Oceania | Continent_Name_South America | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0 | 0 | 0.0 | 4 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 0 | 0 | 0.0 | 4 | 2 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 0 | 0 | 0.0 | 4 | 2 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | 0 | 0 | 0.0 | 3 | 1 | ... | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 0 | 0 | 0.0 | 4 | 2 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
5 rows × 28 columns
X=X1.iloc[:,0:5] # SCALED CONTIUOUS VARIABLE
X.head()
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | |
|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 |
X.shape
(63670, 5)
pca1 = PCA(n_components=4).fit_transform(X)
#with pca components
# eps = 0.6, min_samples = 10 gives 4 clusters and silhoutte score is 0.25875075478737775
# Numpy array of all the cluster labels assigned to each data point
db_default = DBSCAN(eps = 0.6, min_samples = 10).fit(pca1)
labels1 = db_default.labels_
labels1
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels1)) - (1 if -1 in labels1 else 0)
n_noise_ = list(labels1).count(-1)
print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)
Estimated number of clusters: 4 Estimated number of noise points: 101
from sklearn import metrics
print(metrics.silhouette_score(pca1,labels1))
0.25875075478737775
df1 = X.copy(deep=True)
df1['label'] = labels1
df1
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | label | |
|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 0 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 0 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 0 |
| ... | ... | ... | ... | ... | ... | ... |
| 63665 | 0.041387 | -1.547764 | -0.733896 | -0.778844 | 0.944011 | 0 |
| 63666 | 0.115001 | -1.547764 | 0.044904 | -0.914618 | -1.030075 | 0 |
| 63667 | 0.335844 | -1.547764 | 0.319129 | -0.116844 | 0.362986 | 0 |
| 63668 | -0.032227 | -1.547764 | 0.242346 | -0.157165 | 2.666086 | 0 |
| 63669 | 1.808129 | -1.547764 | 0.242346 | 2.534449 | -0.988073 | 0 |
63670 rows × 6 columns
import seaborn as sns
sns.pairplot(df1)
<seaborn.axisgrid.PairGrid at 0x1c5029ca4c0>
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df1['AverageLeadTime'],df1['LodgingRevenue'],hue=df1['label'],palette="tab10")
plt.legend()
plt.show()
# eps = 0.9, min_samples = 20 gives 2 clusters and silhoutte score is 0.43359443420521376
db_default = DBSCAN(eps = 0.9, min_samples =20).fit(pca1)
labels2 = db_default.labels_
labels2
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels2)) - (1 if -1 in labels2 else 0)
n_noise_ = list(labels2).count(-1)
print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)
Estimated number of clusters: 2 Estimated number of noise points: 27
from sklearn import metrics
print(metrics.silhouette_score(pca1,labels2))
0.43359443420521376
df2 = X.copy(deep=True)
df2['label'] = labels2
df2
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | label | |
|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 0 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 0 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 0 |
| ... | ... | ... | ... | ... | ... | ... |
| 63665 | 0.041387 | -1.547764 | -0.733896 | -0.778844 | 0.944011 | 0 |
| 63666 | 0.115001 | -1.547764 | 0.044904 | -0.914618 | -1.030075 | 0 |
| 63667 | 0.335844 | -1.547764 | 0.319129 | -0.116844 | 0.362986 | 0 |
| 63668 | -0.032227 | -1.547764 | 0.242346 | -0.157165 | 2.666086 | 0 |
| 63669 | 1.808129 | -1.547764 | 0.242346 | 2.534449 | -0.988073 | 0 |
63670 rows × 6 columns
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df2['AverageLeadTime'],df2['LodgingRevenue'],hue=df2['label'])
plt.legend()
plt.show()
#without pca components
db_default = DBSCAN(eps = 0.9, min_samples = 10).fit(X)
labels3 = db_default.labels_
labels3
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels3)) - (1 if -1 in labels3 else 0)
n_noise_ = list(labels3).count(-1)
print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)
Estimated number of clusters: 2 Estimated number of noise points: 49
from sklearn import metrics
print(metrics.silhouette_score(X,labels3))
0.3901207693250093
df3 = X.copy(deep=True)
df3['label'] = labels3
df3
| Age | DaysSinceCreation | AverageLeadTime | LodgingRevenue | OtherRevenue | label | |
|---|---|---|---|---|---|---|
| 0 | 0.262230 | -1.265417 | -0.459671 | 0.023456 | 0.360185 | 0 |
| 1 | 0.262230 | 1.581582 | -0.284167 | -0.350951 | -0.372046 | 0 |
| 2 | 0.924758 | 1.581582 | 0.066842 | -0.515525 | -0.274042 | 0 |
| 3 | 0.483072 | 1.581582 | -0.317074 | -0.556669 | -0.778064 | 0 |
| 4 | -1.136441 | 1.581582 | -0.536454 | 0.698211 | 0.201979 | 0 |
| ... | ... | ... | ... | ... | ... | ... |
| 63665 | 0.041387 | -1.547764 | -0.733896 | -0.778844 | 0.944011 | 0 |
| 63666 | 0.115001 | -1.547764 | 0.044904 | -0.914618 | -1.030075 | 0 |
| 63667 | 0.335844 | -1.547764 | 0.319129 | -0.116844 | 0.362986 | 0 |
| 63668 | -0.032227 | -1.547764 | 0.242346 | -0.157165 | 2.666086 | 0 |
| 63669 | 1.808129 | -1.547764 | 0.242346 | 2.534449 | -0.988073 | 0 |
63670 rows × 6 columns
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df3['AverageLeadTime'],df3['LodgingRevenue'],hue=df3['label'],palette="tab10")
plt.legend()
plt.show()
from sklearn.metrics import accuracy_score
#accuracy_score(y, y_cluster)
from sklearn.tree import DecisionTreeClassifier
X = df2.drop(['label'], axis=1)
y = df2['label']
from sklearn.model_selection import train_test_split
# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)
model1 = DecisionTreeClassifier()
model1 = model1.fit(X_train, y_train)
ypred = model1.predict(X_test)
accuracy_score(y_test, ypred)
0.9997382336003351
from sklearn.ensemble import BaggingClassifier
model2 = BaggingClassifier()
model2 = model2.fit(X_train, y_train)
from sklearn import metrics
from sklearn.metrics import davies_bouldin_score
ypred = model2.predict(X_test)
accuracy_score(y_test, ypred)
0.9996858803204021
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
X = df1.drop(['label'], axis=1)
y = df1['label']
from sklearn.model_selection import train_test_split
# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)
model1 = DecisionTreeClassifier()
model1 = model1.fit(X_train, y_train)
ypred = model1.predict(X_test)
accuracy_score(y_test, ypred)
0.9986911680016753
from sklearn.ensemble import BaggingClassifier
model2 = BaggingClassifier()
model2 = model2.fit(X_train, y_train)
from sklearn import metrics
from sklearn.metrics import davies_bouldin_score
ypred = model2.predict(X_test)
accuracy_score(y_test, ypred)
0.9991099942411392
import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
plt.style.use('seaborn')
data=pd.read_csv('processed_data.csv')
data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
'DaysSinceFirstStay']]
import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
plt.style.use('seaborn')
data=pd.read_csv('processed_data.csv')
data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
'DaysSinceFirstStay']]
# Dimensionality reduction
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture
# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)
explained_variance = pca.explained_variance_
# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()
pca.explained_variance_ratio_
array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
3.44155412e-05])
#Scree plot
import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))
num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100
ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)
for i in range(9):
ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))
ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')
ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)
(-1.6246438957118616, 37.56515055083924)
pca = PCA().fit(data1)
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)
fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')
plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '90% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()
[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055 0.99891343 0.99996558 1. ]
pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)
from scipy.cluster.hierarchy import linkage, dendrogram
plt.figure(figsize=[10,10])
merg = linkage(x_7cols, method='ward')
dendrogram(merg, leaf_rotation=90)
plt.title('Dendrogram')
plt.xlabel('Data Points')
plt.ylabel('Euclidean Distances')
plt.show()
# Initializes and predicts labels
ac_labels = AgglomerativeClustering(n_clusters=4, linkage='ward').fit_predict(x_7cols)
# Calculates average silhouette coefficient
silhouette_ac = silhouette_score(x_7cols, ac_labels)
# Creates dataframe of the labels for later use
agglo_df = pd.DataFrame(ac_labels, columns=['cluster_id'])
print('The Silhouette coefficient for the Agglomerative Hierarchical Clustering algorithm is '+str(round(silhouette_ac, 2)))
# Creates plot of cluster sizes
agglo_df.value_counts().sort_index().plot.bar(figsize=(7, 6))
plt.title('Agglomerative clusters size distribution', fontsize=14)
plt.xlabel('Cluster', fontsize=13)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','k 4'], rotation=0)
plt.show()
The Silhouette coefficient for the Agglomerative Hierarchical Clustering algorithm is 0.2
unscaled_data=pd.read_csv('Before_scaling_data.csv')
df = unscaled_data.copy(deep=True)
df['label'] = ac_labels
unscaled_data['PersonsNights'].unique()
array([4, 3, 1, 0, 2])
plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],palette="tab10")
plt.legend()
plt.show()
plt_1=plt.figure(figsize=(15,25))
sns.relplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],
col=df['PersonsNights'],palette="tab10",col_wrap=2)
plt.show()
<Figure size 1080x1800 with 0 Axes>
from sklearn import metrics
from sklearn.metrics import davies_bouldin_score
#SILHOUETTE SCORE
metrics.silhouette_score(x_7cols, ac_labels, metric='euclidean')
0.19836000986864044
#Calinski-Harabasz Index
metrics.calinski_harabasz_score(x_7cols, ac_labels)
13938.022724703173
#Davies-Bouldin Index
davies_bouldin_score(x_7cols, ac_labels)
1.495576493288908